class: top, left, inverse, title-slide .title[ # Untangling biodiversity changes
across a continuum of spatial scales ] .subtitle[ ##
PhD Presentation ] .author[ ###
PhD candidate: François Leroy
Supervisor: Petr Keil ] .institute[ ### Czech University of Life Sciences
Prague ] --- # Biodiversity changes are scale dependent * **Global** biodiversity is **declining** -- * **Local, regional or national** trends are **not always** similar <img src="data:image/png;base64,#images/litrev.jpg" height="430px" /> --- # Biodiversity changes are scale dependent * **Global** biodiversity is **declining** * **Local, regional or national** trends are **not always** similar <br><br><br> .center[ ### `\(\Rightarrow\)` Dynamic processes (*i.e.* colonization, extinction, turnover...) vary with spatial scales ] -- <br> .center[ ### `\(\Rightarrow\)` Biodiversity trends have to be assessed across spatial scales ] --- class: inverse, center, middle # Objectives <br><br> ### `\(\Rightarrow\)` How are avian biodiversity changes scale dependent across Czechia? <br><br> ### `\(\Rightarrow\)` Why do we observe this scale dependency of biodiversity changes? --- # Scales * **Spatial scale** `\(=\)` **Spatial grain** <br><br><br> .center[ <!-- --> ] --- # Scales * **Temporal scale** `\(=\)` **Temporal grain** <br><br><br> .center[ <img src="data:image/png;base64,#images/temporalgrain.jpg" width="80%" /> ] --- # Biodiversity data * One dataset express the biodiversity at its specific spatial and temporal grains * Data heterogeneity in spatial and temporal grains and extent * Lack of data -- <br><br> ## Problem: With the actual data, it is not straight forward to assess biodiversity trends for a continuum of spatial scales --- # Spatial aggregation **Jarzyna *et al.* (2015)** <br><br><br> <img src="data:image/png;base64,#images/jarzyna2015.jpg" height="150%" /> --- # Spatial aggregation **Chase *et al.* (2019)** .center[ <img src="data:image/png;base64,#images/chase_2019.PNG" width="75%" /> ] --- # Model .pull-left[ * Use biodiversity data with heterogeneous: **spatial grain, temporal grain, location, spatial extent and temporal extent**. <br><br> * Use this component as **covariates** to predict species richness at desired (spatial & temporal) grain and location (in space & time) ] .pull-right[ <img src="data:image/png;base64,#images/keil_chase_title.PNG" width="1529" /> .center[ <img src="data:image/png;base64,#images/keil_chase_2022.PNG" width="75%" /> ] ] --- # Model <br><br> **In practice:** ``` treeBasedModel(species richness ~ area, -> Species-area relationship temporal grain, -> Species-time relationship latitude, -> Location in space longitude, -> Location in space date) -> Location in time ``` <br> Tree based models: the flexibility grasps the interactions between **species area/time relationship** and their location in space and time. The species-area and species-time relationships allows to down/upscale species richness -- <br> .center[ ### `\(\Rightarrow\)` We need data at different spatial and temporal grains ] --- # Bird atlas of Czech Republic .pull-left[ .center[**Spatial scales**] Large scale dataset. Ranging from less than **100 Km** `\(^2\)` to **80 000 Km** `\(^2\)` (the entire Czech Republic) <!-- --> ] .footnote[ Courtesy of Vladimír Bejček, Karel Šťastný and Ivan Mikuláš ] .pull-right[ .center[**Temporal scales**] 3 time periods, 3 different time spans: * M2 = 1985-1989 (**5 years**) * M3 = 2001-2003 (**3 years**) * M4 = 2014-2017 (**4 years**) ] -- <br><br> .center[ ### `\(\Rightarrow\)` The model homogenize the temporal grain and the sampling effort ] --- # Breeding bird survey (BBS)
--- # Breeding bird survey (BBS) dataset .pull-left[ **Spatial scales:** very local **Temporal scales:** from 0.5 year to 10+ years .center[ <br><br> ### `\(\Rightarrow\)` The model predict species richness for missing years ] .footnote[Courtesy of Jiří Reif] ] .pull-right[
] --- # Richness change across scales * For each spatial scale: predictions of species richness from 1987 to 2017 * Assessment of the species richness change per year <br><br> <!-- --> --- # Richness changes across scales .center[ <img src="data:image/png;base64,#images/srtrend.jpg" width="85%" /> ] --- # Richness changes across scales .center[ <!-- --> ] --- # Colonization, extinction, persistence across scales * Species richness change is the sum of these 3 processes * Assessment across scales --- # Colonization, extinction, persistence across scales .center[ ## Extinction <img src="data:image/png;base64,#images/extinction_concept.JPG" width="75%" /> .credit[modified from Keil et al. (2017)] ] --- # Colonization, extinction, persistence across scales .center[ ## Colonization <img src="data:image/png;base64,#images/colonization_concept.JPG" width="68%" /> .credit[modified from Keil et al. (2017)] ] --- # Colonization, extinction, persistence across scales .center[ <img src="data:image/png;base64,#images/CEP.JPG" width="150%" /> ] --- # Colonization, extinction, persistence across scales .center[ <img src="data:image/png;base64,#images/CEP_sr.jpg" width="68%" /> ] --- # Beta-diversity across scales .pull-left[ **Similarity index:** `\(jaccard = \frac{pers}{pers+col+ext}\)` <br><br> <img src="data:image/png;base64,#images/jaccard.JPG" width="100%" /> ] -- .pull-right[ **Dissimilarity index** `\(betasim = \frac{min(ext,col)}{pers + min(ext,col)}\)` <br><br> <!-- --> ] --- # Conclusion This pattern: .center[ <img src="data:image/png;base64,#images/srtrend-copy.jpg" width="75%" /> ] Can be explained by the spatial scaling of dynamic processes: * `\(\nearrow\)` **persistence** with increasing spatial grain * different `\(\searrow\)` slope of **extinction** and **colonization** with increasing spatial grain * `\(\searrow\)` temporal turnover with increasing spatial grain --- # Conclusion .center[ ### `\(\Rightarrow\)` As colonization, extinction and persistence are scale dependent, we observe a scale dependency of biodiversity change ] -- <br><br> .center[ ### `\(\Rightarrow\)` Assessing biodiversity trends at national scale doesn't inform much about local dynamic and vice-versa ] -- <br><br> .center[ ### `\(\Rightarrow\)` Using heterogeneous dataset allows to model biodiversity at location and time where data is missing ] --- class: inverse, center, middle # Thank you for your attention <br><br><br> <svg viewBox="0 0 512 512" style="position:relative;display:inline-block;top:.1em;fill:white;height:1em;" xmlns="http://www.w3.org/2000/svg"> <path d="M464 64H48C21.49 64 0 85.49 0 112v288c0 26.51 21.49 48 48 48h416c26.51 0 48-21.49 48-48V112c0-26.51-21.49-48-48-48zm0 48v40.805c-22.422 18.259-58.168 46.651-134.587 106.49-16.841 13.247-50.201 45.072-73.413 44.701-23.208.375-56.579-31.459-73.413-44.701C106.18 199.465 70.425 171.067 48 152.805V112h416zM48 400V214.398c22.914 18.251 55.409 43.862 104.938 82.646 21.857 17.205 60.134 55.186 103.062 54.955 42.717.231 80.509-37.199 103.053-54.947 49.528-38.783 82.032-64.401 104.947-82.653V400H48z"></path></svg> leroy@fzp.czu.cz <svg viewBox="0 0 512 512" style="position:relative;display:inline-block;top:.1em;fill:lightblue;height:2em;" xmlns="http://www.w3.org/2000/svg"> <path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"></path></svg> @FrsLry <svg viewBox="0 0 496 512" style="position:relative;display:inline-block;top:.1em;fill:white;height:2em;" xmlns="http://www.w3.org/2000/svg"> <path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"></path></svg> @FrsLry <svg viewBox="0 0 320 512" style="position:relative;display:inline-block;top:.1em;fill:white;height:1em;" xmlns="http://www.w3.org/2000/svg"> <path d="M302.189 329.126H196.105l55.831 135.993c3.889 9.428-.555 19.999-9.444 23.999l-49.165 21.427c-9.165 4-19.443-.571-23.332-9.714l-53.053-129.136-86.664 89.138C18.729 472.71 0 463.554 0 447.977V18.299C0 1.899 19.921-6.096 30.277 5.443l284.412 292.542c11.472 11.179 3.007 31.141-12.5 31.141z"></path></svg> https://frslry.github.io/ --- class: inverse, center, middle # Supplementary slides --- # Performance .pull-left[ **Atlas model** `\(XGBoost\)` `\(R^2 = 0.77\)` `\(MAE = 9\)` <!-- ``` --> <!-- species richness ~ area, --> <!-- temporal grain, --> <!-- latitude, --> <!-- longitude, --> <!-- date, --> <!-- sampling effort, --> <!-- shape, --> <!-- elevation) --> <!-- ``` --> <img src="data:image/png;base64,#images/obsvspred_atlas.JPG" width="90%" /> ] .pull-right[ **BBS model** `\(Random Forest\)` `\(R^2 = 0.74\)` `\(MAE = 10\)` <img src="data:image/png;base64,#images/obsvspred_bbs.JPG" width="90%" /> ] --- # And what about temporal scaling? .center[ <img src="data:image/png;base64,#images/temporal_scaling.jpg" width="85%" /> ]